About
In making advances within Computational Systems Biology there is an acknowledged need for the ongoing development of both probabilistic and mechanistic, possibly multi-scale, models of complex biological processes. In addition to such models the development of appropriate and efficient inferential methodology to identify and reason over such models is necessary. Examples of the progress which has been made in our understanding of modern biology by the exploitation of such methodology include model based inference of p53 activity; uncovering the evolution of protein complexes and understanding the circadian clock in plants; details of which were presented at the LICSB workshops.
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Uploaded videos:
Welcome
Introduction
Apr 17, 2008
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3185 Views
Session 1
Gaussian process modelling of transcription factor networks using Markov Chain M...
Apr 17, 2008
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4499 Views
Data variability could be your friend
Apr 17, 2008
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5475 Views
Time delay analysis
Apr 17, 2008
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5647 Views
Gaussian process modelling of latent chemical species: Applications to inferring...
Apr 17, 2008
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3124 Views
Session 2
Learning Bayesian networks from postgenomic data with an improved structure MCMC...
Apr 17, 2008
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5938 Views
Statistical learning of biological networks: a brief overview
Apr 17, 2008
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4475 Views
Validating inferred gene networks using ODE models of regulation dynamics
Apr 17, 2008
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4163 Views
Relationship between structure and dynamics of gene regulatory networks
Apr 17, 2008
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5517 Views
Session 3
Parameter estimation using moment-closure methods
Apr 17, 2008
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4346 Views
Parameter estimation in biochemical reaction networks: An observer-based approac...
Apr 17, 2008
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3838 Views
BioBayes: Bayesian inference for Systems Biology
Apr 17, 2008
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419119 Views
Session 4
Factor models for QTL studies
Apr 17, 2008
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5385 Views
Probabilistic multi-class multi-kernel learning: On protein fold recognition and...
Apr 17, 2008
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4124 Views
Predicting anti-cancer molecule activity using machine learning algorithms
Apr 17, 2008
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419336 Views